Environmental factors such as tobacco smoking may have long-lasting effects on DNA methylation patterns, which might lead to changes in gene expression and in a broader context to the development or ...progression of various diseases. We conducted an epigenome-wide association study (EWAs) comparing current, former and never smokers from 1793 participants of the population-based KORA F4 panel, with replication in 479 participants from the KORA F3 panel, carried out by the 450K BeadChip with genomic DNA obtained from whole blood. We observed wide-spread differences in the degree of site-specific methylation (with p-values ranging from 9.31E-08 to 2.54E-182) as a function of tobacco smoking in each of the 22 autosomes, with the percent of variance explained by smoking ranging from 1.31 to 41.02. Depending on cessation time and pack-years, methylation levels in former smokers were found to be close to the ones seen in never smokers. In addition, methylation-specific protein binding patterns were observed for cg05575921 within AHRR, which had the highest level of detectable changes in DNA methylation associated with tobacco smoking (-24.40% methylation; p = 2.54E-182), suggesting a regulatory role for gene expression. The results of our study confirm the broad effect of tobacco smoking on the human organism, but also show that quitting tobacco smoking presumably allows regaining the DNA methylation state of never smokers.
The delta-5 and delta-6 desaturases have long been known to be important enzymes in the endogenous formation of long-chain polyunsaturated fatty acids (LC-PUFAs). Cloning of the coding sequences and ...chromosomal localization of the desaturase encoding genes fatty acid desaturase 1 and 2 (FADS1 and FADS2) opened the way for analyses of genetic factors as regulators of desaturase activity and LC-PUFA homeostasis. The present review summarizes the recent association studies on FADS genotypes and LC-PUFA levels and suggests ideas how FADS genotypes can be integrated in future research.
An initial candidate gene study reported highly significant associations between FADS gene cluster polymorphisms and fatty acid levels in serum phospholipids with an extraordinary high genetically explained variance for arachidonic acid levels of 28.5%. Carriers of the minor alleles had enhanced levels of desaturase substrates and decreased levels of desaturase products, suggesting a decline in desaturase expression or activity because of the polymorphisms. These results were replicated in several association studies additionally showing an effect in different human tissues as well as in a recent genome-wide association study on LC-PUFA levels.
The validated strong association between FADS genotypes and fatty acid levels in diverse human tissues shows that FADS gene cluster polymorphisms are, in addition to nutritional regulation of fatty acid synthesis, a very important regulator of LC-PUFA synthesis.
Through the measure of thousands of small-molecule metabolites in diverse biological systems, metabolomics now offers the potential for new insights into the factors that contribute to complex human ...diseases such as cardiovascular disease. Targeted metabolomics methods have already identified new molecular markers and metabolomic signatures of cardiovascular disease risk (including branched-chain amino acids, select unsaturated lipid species, and trimethylamine-
-oxide), thus in effect linking diverse exposures such as those from dietary intake and the microbiota with cardiometabolic traits. As technologies for metabolomics continue to evolve, the depth and breadth of small-molecule metabolite profiling in complex systems continue to advance rapidly, along with prospects for ongoing discovery. Current challenges facing the field of metabolomics include scaling throughput and technical capacity for metabolomics approaches, bioinformatic and chemoinformatic tools for handling large-scale metabolomics data, methods for elucidating the biochemical structure and function of novel metabolites, and strategies for determining the true clinical relevance of metabolites observed in association with cardiovascular disease outcomes. Progress made in addressing these challenges will allow metabolomics the potential to substantially affect diagnostics and therapeutics in cardiovascular medicine.
Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize ...deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability.
Abstract Objective MicroRNAs are small non-coding RNAs that inversely regulate their target gene expression. The whole miRNA profile of human atherosclerotic plaques has not been studied previously. ...The aim of this study was to investigate the miRNA expression profile in human atherosclerotic plaques as compared to non-atherosclerotic left internal thoracic arteries (LITA), and to connect this expression to the processes in atherosclerosis. Methods The miRNA expression profiles of six LITAs and 12 atherosclerotic plaques obtained from aortic, carotid, and femoral atherosclerotic arteries from Tampere Vascular Study were analyzed. The analyses were performed with Agilent's miRNA Microarray. The expression levels of over 4-fold up-regulated miRNAs were verified with qRT-PCR from a larger population ( n = 50). Messenger RNA levels were analyzed with Illumina's Expression BeadChip to study miRNA target expression. Results Ten miRNAs were found to be differently expressed in atherosclerotic plaques when compared to controls ( p < 0.05). The expression of miR-21, -34a, -146a, -146b-5p, and -210 was verified and found to be significantly up-regulated in atherosclerotic arteries versus LITAs ( p < 0.001, fold changes 4.61, 2.55, 2.87, 2.82, and 3.92, respectively). Several predicted targets of these miRNAs were down-regulated, and gene set enrichment analysis showed several pathways which could be differently expressed due to this miRNA profile. Conclusions The microRNA expression profile differs significantly between atherosclerotic plaques and control arteries. The most up-regulated miRNAs are involved in processes known to be connected to atherosclerosis. Interfering with the miRNA expression in the artery wall is a potential way to affect atherosclerotic plaque and cardiovascular disease development.
Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often ...lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.
The SARS-CoV-2 coronavirus has led to a pandemic with millions of people affected. The present study finds that risk-factors for severe COVID-19 disease courses, i.e. male sex, older age and ...sedentary life style are associated with higher prostaglandin E2 (PGE2) serum levels in blood samples from unaffected subjects. In COVID-19 patients, PGE2 blood levels are markedly elevated and correlate positively with disease severity. SARS-CoV-2 induces PGE2 generation and secretion in infected lung epithelial cells by upregulating cyclo-oxygenase (COX)-2 and reducing the PG-degrading enzyme 15-hydroxyprostaglandin-dehydrogenase. Also living human precision cut lung slices (PCLS) infected with SARS-CoV-2 display upregulated COX-2. Regular exercise in aged individuals lowers PGE2 serum levels, which leads to increased Paired-Box-Protein-Pax-5 (PAX5) expression, a master regulator of B-cell survival, proliferation and differentiation also towards long lived memory B-cells, in human pre-B-cell lines. Moreover, PGE2 levels in serum of COVID-19 patients lowers the expression of PAX5 in human pre-B-cell lines. The PGE2 inhibitor Taxifolin reduces SARS-CoV-2-induced PGE2 production. In conclusion, SARS-CoV-2, male sex, old age, and sedentary life style increase PGE2 levels, which may reduce the early anti-viral defense as well as the development of immunity promoting severe disease courses and multiple infections. Regular exercise and Taxifolin treatment may reduce these risks and prevent severe disease courses.
BACKGROUND: Blood and tissue long-chain polyunsaturated fatty acid (LC-PUFA) amounts, which have been associated with early development and lifelong health, depend on dietary intake and endogenous ...conversion of precursor fatty acids (FAs) by the enzymes Δ⁵-desaturase and Δ⁶-desaturase. Polymorphisms in the desaturase encoding genes FADS1 and FADS2 have been associated with several n-6 (omega-6) and n-3 (omega-3) FAs and especially with arachidonic acid (AA) amounts. Associations with docosahexaenoic acid (DHA), which is considered particularly important for brain and retina development, are hardly existent. OBJECTIVE: We explored the relation between FADS gene cluster polymorphisms and red blood cell (RBC) FA amounts in >4000 pregnant women participating in the Avon Longitudinal Study of Parents and Children. DESIGN: Linear regression analysis of 17 single nucleotide polymorphisms (SNPs) in the FADS gene cluster was conducted with RBC phospholipid FAs from 6711 samples from 4457 women obtained throughout pregnancy (mean ± SD gestational age: 26.8 ± 8.2 wk). RESULTS: Independent of dietary effects, the minor alleles were consistently positively associated with precursor FAs and negatively associated with LC-PUFAs and product:substrate ratios of the n-6 (AA:linoleic acid ratio) and n-3 (eicosapentaenoic acid:α-linolenic acid ratio) pathways. In contrast to previous studies, we also showed significant inverse associations with DHA. Similar but weaker associations were shown for the FADS3 SNP rs174455. CONCLUSIONS: FADS genotypes influence DHA amounts in maternal RBC phospholipids and might affect the child's DHA supply during pregnancy. It is highly likely that a gene product of FADS3 has a desaturating activity.
Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects ...of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.
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